Source: https://ailoot.ai/models/en/amazon-titan-embed-image-v1/ Canonical: https://ailoot.ai/models/en/amazon-titan-embed-image-v1/ Language: en # Amazon: Titan Multimodal Embeddings G1 [Markdown](https://ailoot.ai/models/en/amazon-titan-embed-image-v1/index.md) · [JSON](https://ailoot.ai/models/en/amazon-titan-embed-image-v1/facts.json) ## [Amazon: Titan Multimodal Embeddings G1](https://ailoot.ai/models/en/amazon-titan-embed-image-v1/) `amazon/titan-embed-image-v1` AmazonEmbeddingsInput $0.0016 / 1KOutput — Creates vectors from short text for semantic search and recommendations in Titan’s multimodal embedding space. ### Text input into a multimodal space Amazon Titan Multimodal Embeddings G1 is an Amazon embedding model with a shared representation space for text and images. This Ailoot API listing provides a text-based workflow: submit a short description or search phrase and receive a numerical vector. It represents the text’s content in a form your application can compare with other compatible vectors. ### What you can use it for The model can form part of catalog search, recommendations and personalization. For example, the description “red travel bag” becomes a search vector that your application uses to find similar entries in a prepared index. Search results depend on both the catalog’s contents and how it is indexed: the embedding represents the query, while the search system assembles the actual selection. This version of Titan is designed around a shared space for textual and visual data. A text vector can be useful in a system where compatible object representations have already been prepared. For conventional search across text documents, chunk length and language suitability also matter: the developer describes G1 as an English-language model with a short text input. ### How to organize the workflow 1. Express a single object, attribute or search intent in a short phrase. 2. Obtain an embedding for that phrase. 3. Compare it with the vectors in your index and select suitable entries. 4. Pass the retrieved material to the next stage, such as a catalog interface or a separate model that writes an answer. The index and query must use compatible data representations. Before populating a large catalog, test real search phrases for semantic matches, uncommon attributes and cases where similar words describe different objects. ### Input and output in Ailoot API The method available here accepts **text** and returns an **embedding**; this listing has an input limit of **128 tokens**. Use brief descriptions for this operation and split long documents into separate, meaningful chunks in advance. Image uploads, image generation and video processing are outside this method’s scope. “Multimodal” describes the underlying model; the workflow available through this listing creates a vector from text. ### Available through Ailoot API Input types `text` Output types `embedding` Context 128 *tokens* Max. output Not specified in the catalogue Input $0.0016 */ 1K* `amazon/titan-embed-image-v1` · JSON ``` {"about":"### Text input into a multimodal space\n\nAmazon Titan Multimodal Embeddings G1 is an Amazon embedding model with a shared representation space for text and images. This Ailoot API listing provides a text-based workflow: submit a short description or search phrase and receive a numerical vector. It represents the text’s content in a form your application can compare with other compatible vectors.\n\n### What you can use it for\n\nThe model can form part of catalog search, recommendations and personalization. For example, the description “red travel bag” becomes a search vector that your application uses to find similar entries in a prepared index. Search results depend on both the catalog’s contents and how it is indexed: the embedding represents the query, while the search system assembles the actual selection.\n\nThis version of Titan is designed around a shared space for textual and visual data. A text vector can be useful in a system where compatible object representations have already been prepared. For conventional search across text documents, chunk length and language suitability also matter: the developer describes G1 as an English-language model with a short text input.\n\n### How to organize the workflow\n\n1. Express a single object, attribute or search intent in a short phrase.\n2. Obtain an embedding for that phrase.\n3. Compare it with the vectors in your index and select suitable entries.\n4. Pass the retrieved material to the next stage, such as a catalog interface or a separate model that writes an answer.\n\nThe index and query must use compatible data representations. Before populating a large catalog, test real search phrases for semantic matches, uncommon attributes and cases where similar words describe different objects.\n\n### Input and output in Ailoot API\n\nThe method available here accepts **text** and returns an **embedding**; this listing has an input limit of **128 tokens**. Use brief descriptions for this operation and split long documents into separate, meaningful chunks in advance. Image uploads, image generation and video processing are outside this method’s scope. “Multimodal” describes the underlying model; the workflow available through this listing creates a vector from text.","alias_of":null,"aliases":[],"author":"Amazon","canonical_model":"amazon/titan-embed-image-v1","ctx":128,"formats":["embeddings"],"id":"amazon/titan-embed-image-v1","input":["text"],"kind":"embedding","max_out":0,"methods":[{"cache_read":null,"cache_write":null,"cache_write_1h":null,"cache_write_5m":null,"conditional_meter":null,"ctx":128,"family":"openai","fast_multiplier":1,"flex_multiplier":1,"fmt":"embeddings","http":"POST","img_in":null,"img_out":null,"in":1.6,"input":["text"],"label":"Эмбеддинги","max_out":0,"max_tool_calls":null,"out":0,"output":["embedding"],"path":"/v1/embeddings","request_model_id":"amazon/titan-embed-image-v1","route_label":"Ultra","status_reads":[],"tiers":[],"tool_prices":null,"unit":"token","units":[]}],"model_aliases":[{"canonical_model_id":"amazon/titan-embed-image-v1","request_model_id":"amazon/titan-embed-image-v1","route_label":"Ultra"}],"name":"Amazon: Titan Multimodal Embeddings G1","price":{"in":1.6,"in_label":null,"out":null,"out_label":null,"sort_in":1.6,"sort_out":null,"varies":false},"public_id":"amazon/titan-embed-image-v1","short":"Creates vectors from short text for semantic search and recommendations in Titan’s multimodal embedding space."} ``` ## Questions How do I choose a model for my task? First identify the required input and output: text, image, audio or video. Then compare compatible API methods, the exact model ID or current alias target, price and billing unit. GET /v1/models is free and lists models available to your key. Does each model support every API method and media type? Use the API methods and input/output types listed on the model card. Support for text, images, audio or video depends on the selected model and method; check their request example before sending your data. What is the difference between a model ID and an alias? An exact model ID names a specific model. An alias points to a model and its target can change. Check the current alias target in the catalogue; use the exact ID when your integration needs a specific model. How do I compare prices before sending a request? Check the model card for the price and billing unit of the selected API method. Compare the same unit and input/output type; request history shows actual usage and the charged amount. What does a subscription give me? A discount on token purchases for regular use. Choose a plan for your request volume; subscription terms and pricing are shown at checkout. How do I apply a promo code for up to 50% off? Enter the promo code when buying tokens. The total updates according to the promotion terms; you can see the discount before paying. ## Ailoot · The company and its project portfolio Ailoot combines established operating experience, a large client base, stable service and ongoing KPI control. The company and its project portfolio: millions of customers; monthly sales in the billions. Ailoot brings that operating experience to API access for AI features in customer products, agents, applications and automations.